Have you heard about the incredible progress that Snowflake has made with integrating AI and large language models (LLMs) into its platform? Snowflake’s integration of AI and LLMs showcases a leading example of AI implementation in business and is rapidly setting new standards in data cloud technology.
This recent adaptation by Snowflake marks a significant milestone in AI implementation in business. It demonstrates how LLMs can be applied and monetized very quickly in a practical business context.
Let’s take a deeper look under the hood here, shall we?
Advancing Business with AI: Snowflake’s Role in Pioneering AI Implementation in Business
Snowflake’s AI integration involves the use of generative AI and LLMs to enhance data-driven decisions and maximize the value of data that sits on its platform. For this use case, generative AI and LLMs are being deployed to identify the right data points, assets, and insights, thereby empowering teams to make maximum value from the data that’s sitting within their repositories.
But how did Snowflake manage to adapt so quickly, you ask? Strategic acquisition of course!
But how did Snowflake manage to adapt so quickly, you ask? Strategic acquisition of course!
Simply put, the strategic acquisitions made by Snowflake underline the importance of AI implementation in business, particularly in enhancing data analytics and decision-making processes.
Snowflake recently acquired three companies to bring advanced AI and deep learning to its Data Cloud. Those three companies:
- Neeva: A search startup that leverages generative AI to enable users to query and discover data in new ways
- LeapYear: A company that enhances Snowflake’s data clean room capabilities
- Myst AI: An artificial intelligence-based time series forecasting platform provider
This move is part of Snowflake’s strategy to stay at the forefront of the AI trend; A trend which is expected to see a massive $1.3 trillion spend over the next decade (according to Bloomberg, June 2023)
Snowflake’s commitment to AI implementation in business is also evident in their recent launch of Snowflake Cortex, aimed at custom AI development for companies.
Snowflake’s AI integration is just one of hundreds of real-life cases where AI implementation in business is generating massive returns very quickly.
If you’re lacking in development skills that are required to integrate LLMs into your company’s applications, don’t feel bad. This is a very new space and there is still time for you to jump onboard and get ahead of the pack!
That’s one reason I’m so excited to bring to you this week’s incredible free learning opportunity: How to Launch ChatGPT LLM Apps in 3 Easy Steps
Free Training Invite: How to Launch ChatGPT LLM Apps in 3 Easy Steps
This training session will focus on the essentials of AI implementation in business, particularly on integrating ChatGPT LLMs into corporate applications.
Here’s what you’ll come away with:
- See first-hand the potential of LLMs in enhancing data-driven decisions.
- Get a simple 3-step process you can use for integrating LLMs into your applications
- Learn industry best practices for developing and testing LLM apps.
Available on-demand, join us for a power-packed training session where you’ll learn how to get started building and launching ChatGPT LLM applications almost overnight.
This event is meticulously designed for developers, data professionals, and AI enthusiasts like yourself.
As always with SingleStore trainings, you’ll be getting hands-on knowledge straight from the experts.
Plus, don’t miss out on our live demo and code-sharing segment for a practical experience in deploying these sophisticated technologies.
This training is key to elevating your skills so you can stay at the forefront of AI application development.
Click here to reserve your seat now and take the first step on your journey to mastering ChatGPT LLM applications.
Pro-tip: If you like this training on AI implementation in business, consider checking out other free AI app development trainings we are offering here, here, here, here, here, here, here, here, here,here, and here.
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